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Data Engineer

Data·Full-time·Canada(Toronto, Vancouver)/Hybrid , India(Bangalore)/Hybrid, USA(Fremont-California)/Hybrid

Bring your 5+ years of data engineering expertise to a team building scalable, production-grade data platforms that support critical business and AI initiatives. You will work with modern data technologies to develop reliable pipelines and high-performance data solutions.

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As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data pipelines and platforms that enable analytics, business intelligence, and machine learning applications. You will collaborate with data scientists, analysts, software engineers, and business stakeholders to ensure data is reliable, accessible, secure, and optimized for business use.

Key Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.
Build and optimize data ingestion, transformation, and processing systems.
Develop efficient data models for analytics and reporting.
Work with structured and unstructured data from multiple sources.
Design and maintain data warehouses, data lakes, and modern data platforms.
Optimize SQL queries and data-processing jobs for performance and scalability.
Implement data quality, validation, monitoring, and governance processes.
Integrate data from APIs, databases, applications, and third-party systems.
Work with cloud-based data services and distributed processing technologies.
Collaborate with data scientists and ML engineers to support AI/ML data requirements.
Ensure data pipelines are scalable, secure, reliable, and maintainable.
Troubleshoot data-related issues and improve pipeline reliability.
Contribute to data architecture and technical design decisions.
Mentor junior data engineers and contribute to engineering best practices.

Required skills and qualifications

5+ years of professional experience in Data Engineering or a related role.
Strong programming skills in Python and advanced SQL.
Hands-on experience developing ETL/ELT pipelines.
Strong understanding of relational and non-relational databases.
Experience with data warehousing and data lake architectures.
Experience with distributed data processing technologies such as Apache Spark.
Experience with at least one major cloud platform such as AWS, Azure, or GCP.
Experience with workflow orchestration tools such as Apache Airflow or similar technologies.
Strong understanding of data modeling, data quality, and data integration.
Experience with Git, CI/CD, and software engineering best practices.
Ability to work on a dynamic, research-oriented team that has concurrent projects.
Experience designing scalable cloud-native data architectures.

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